СШАSenior Machine Learning Engineer (Growth)
3 дн. назад
220k–275k USD / годСШАSenior
machine learningrecommendation systemsembeddingmodel deployment
Senior Machine Learning Engineer role focused on designing and building end-to-end ML solutions to improve user engagement and retention on .
О компании
- plays a uniquely important role in the future of gaming. We're a multiplatform, multigenerational and multiplayer platform that helps people deepen their friendships around games and shared interests, and helps developers build and grow their businesses. We believe games give us a way to have fun with our favorite people, whether listening to music together or grinding in competitive matches for diamond rank. Join us in our mission! Your future is just a click away!
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Обязанности
- Design, build, and scale the recommendation systems that connect new users to communities and content, from candidate generation and ranking through to online serving.
- Solve cold-start personalization for users with little or no history on , turning the limited signals available into recommendations for servers, communities, and content.
- Build and deploy ML models that lift new-user retention, combining purpose-built approaches with shared foundations such as user and server embeddings.
- Collaborate cross-functionally to shape the Onboarding roadmap, balancing fast learning experiments with the long-term complexity and scalability of the systems behind them
Условия
- The base salary range for this full-time position is listed below. This position will also be eligible for equity and benefits. Benefits include medical, dental, and vision insurance; a 401(k) plan; life and disability insurance; paid time off; and paid parental leave. Our salary ranges are determined by role and level. Within the range, individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Please note that the compensation details listed in role postings reflect the base salary only, and do not include equity, or benefits. Applications for this role are accepted on an ongoing basis.
- Base Salary
- $220,000 — $275,000 USD
Другое
- has a highly engaged community of millions of daily active users who use the platform for many different reasons, but there’s one thing that nearly everyone does: play video games. plays a uniquely important role in the future of gaming, and we are focused on making it easier and more fun for people to hang out before, during, and after playing games.
- We're looking for a Senior Machine Learning Engineer to join our Engagement ML team, working as part of the cross-functional Onboarding squad in 's Growth organization. You'll design and build 0→1 end-to-end ML solutions that help brand-new users find their place on : the servers, people, and content that turn a first session into a long-term connection to the product. If this excites you, keep reading!
- 5+ years of experience in Machine Learning, Data Science, or Applied Science, with a track record of shipping recommendation or personalization systems end to end.
- Hands-on experience building modern recommendation systems: retrieval and ranking pipelines, embedding-based retrieval (e.g. two-tower models), and deep learning models (e.g. transformers, multi-task learning).
- Strong proficiency in Python and ML frameworks such as PyTorch, JAX, or TensorFlow.
- Experience taking ML systems from ideation to production at scale, including feature pipelines, model serving, monitoring, and online experimentation.
- Strong product intuition and a passion for building 0→1 ML applications grounded in user feedback and real-world impact.
- Excellent communication and collaboration skills, able to serve as the ML voice on a cross-functional squad and keep stakeholders aligned on trade-offs, priorities, and progress.
- The ability to thrive in ambiguous environments, energized by tackling open-ended, technically challenging problems
- Bonus Points If You Have:
- Experience with cold-start or new-user personalization, onboarding flows, or preference elicitation.
- Built user representation models or applied social-graph signals to recommendation problems.
- Worked on growth or engagement problems for consumer products, such as community and content recommendations, user activation, or retention.
- Experience with large-scale recommendation systems, data infrastructure, distributed computing, and vector search.